Owing to the shortcomings of single view angle and insufficient information in the monostatic radar, it is difficult to effectively suppress the deception-false-target (FT) jamming. To counter the range-velocity compound deception jamming, a suppression method based on the radar position error (RPE) is proposed for active $/$ passive netted radar on different moving platforms. In this algorithm, FTs are discriminated in the two stages of track initiation and target tracking. Firstly, in the track initiation stage, the RPE is taken into consideration to construct the error covariance matrix of the Mahalanobis distance. Then, the chi-square test is used to make the measurement correlation based on the azimuth and pitch angle information, which eliminates some FTs. Secondly, the measurements in active radar, which has been successfully correlated, are regarded as the supplementary for track initiation of passive radar. The FTs are discriminated again based on the radial velocity information. Finally, in the target tracking stage, the targets are tracked independently in active radar and passive radar. The sequential track correlation method is adapted to eliminate the remaining FTs that have been maintained in active radar. Simulation results verify that the proposed method improves the discrimination probability of FTs under the condition of ensuring the high discrimination probability of the physical target (PT).

Compared with the multisite radar, the monostatic radar has the disadvantages of a single viewing angle and less available information. In the presence of main-lobe jamming, the monostatic radar has the problems of poor anti-jamming effect and difficulty in achieving target detection. To solve the above problems, the radar signal processing method and data fusion method are combined in this paper. A main-lobe jamming suppression method for phased array (PA) netted radar based on the maximum signal-to-noise ratio blind source separation (MSNR-BSS) is proposed. Firstly, the received signals of a monostatic PA radar are separated into target signals and jamming signals by the MSNR-BSS method. Secondly, channel accumulation, pulse compression, and constant false-alarm rate (CFAR) target detection are performed on the separated signal channels to obtain the target radial distance. Finally, the data fusion theory of three global orientations is employed to determine the target spatial position. Simulation experiments evaluate the performance of main-lobe jamming suppression and verify the effectiveness of the proposed method. In the presence of noise amplitude modulation (NAM) jamming or smeared spectrum (SMSP) jamming (JNR=100dB), the target positioning accuracy of the proposed method is 6.613m or 6.596m. And the peak sidelobe level ratios (PSLR) in the two jamming environments are approximately equal to - 8dB.

The Remaining Useful Life (RUL) prediction of engineering equipment is bound to face the situation of missing data. The existing methods of RUL prediction for such cases mainly take “data generation—RUL prediction” as the basic idea but are often limited to the generation of one-dimensional test data, resulting in the extraction of the prediction network. Therefore, this paper proposes a multivariate degradation device based on Nonlinear Independent Components Estimation (NICE) and the Temporal Convolutional Network–Bidirectional Long Short-term Memory (TCN-BiLSTM) network for the RUL prediction requirements in the case of missing data. First, based on the NICE network, realistic data are generated through reversible sampling; then, the filling of multivariate missing data is completed. Next, the filled multivariate degradation data are processed to generate multivariate degradation data and predicted labels for constructing the training set and test set. Based on this, a residual life prediction model integrating TCN and the BiLSTM network is proposed. To evaluate the proposed method, this paper takes an example of the RUL prediction of aeroengines to perform multivariate degradation data-filling and prediction tasks. The results demonstrate the superiority and potential application value of the method.

In order to settle the problems that the teaching effect of traditional teaching design method is not as good as expected, especially in the teaching practice of difficult content, this paper raises a new classroom design method based on L-kolb experiential learning cycle which including five stages: cycle import module, concrete experience module, reflective observation module, abstract generalization and active examination module. According to the new method, the simple and easy-to-understand practical experiences are combined for the explanation of difficult content so as to overcome the shortcomings of the traditional teaching mode. The effectiveness of the proposed teaching design method has been proved by preliminary practice, as it can improve the learning enthusiasm, students' participation rate and the quality of teaching effectively.

In order to evaluate the impact of different warhead shapes on the damage efficiency of semi armour piercing warhead effectively, four common semi armour piercing warhead models are established based on Solidworks, and the deck model is established with reference to the deck data of an aircraft carrier. And then the material setting and grid division are carried out based on Ansys so as to construct the explicit dynamic simulation model. The credibility of the model is verified based on the residual velocity theory after the model being established. Finally, based on the established model, the simulation research on the influence of warhead shape on vertical armour piercing ability is carried out. The results show that under the same velocity, the armour piercing ability of sharp oval and conical warheads are better and their residual velocity are higher.